Second-level optimal control method and apparatus for joint frequency regulation of heterogeneous load resources

Abstract

Disclosed are a second-level optimal control method and apparatus for joint frequency regulation of heterogeneous load resources, and an electronic device, a storage medium, a computer program product and a computer program. The method comprises: establishing a standardized operation model for heterogeneous load resources; on the basis of parameters of the standardized operation model for the resources and historical frequency regulation signal data of a frequency regulation market, predicting frequency regulation signal distribution and price distribution over a future time period; solving an optimal operation model involving the joint participation of multiple heterogeneous resources in energy and frequency regulation markets, in order to determine a baseline energy output and a frequency regulation capacity; constructing an optimal decomposition problem of load resources and performing variable substitution, and calculating the marginal cost and adjustable range of power adjustment for each resource; and in response to receiving a frequency regulation signal sent by a power grid, starting dispatching from the resource having the lowest marginal regulation cost, and updating the power of each load-side resource until power adjustment requirements are met.

Description

Second-level optimization control method and device for heterogeneous load resource joint frequency modulation

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to Chinese Patent Application No. 202410424141X filed on April 9, 2024 in China, the entire contents of which are incorporated herein by reference. TECHNICAL FIELD

[0003] The present disclosure relates to the field of virtual power plant operation, in particular to a second-level optimization control method and device for heterogeneous load resource joint frequency modulation, electronic equipment, storage medium, computer program product and computer program. BACKGROUND

[0004] Power systems around the world are gradually reducing their dependence on fossil fuels, adding a large number of wind and photovoltaic power resources. Under this background, the supply and demand balance of power systems is facing unprecedented challenges, because the output of wind and photovoltaic power is affected by changing weather, unlike traditional thermal power which can be flexibly adjusted. In order to cope with these challenges, an economic and efficient method is to use the flexibility of demand-side resources, such as temperature-controlled loads, electric vehicles and industrial production processes. However, due to the relatively small capacity and weak control ability of individual demand-side resources, they usually need to be aggregated and controlled through virtual power plants as a platform. Although not a physical power plant, virtual power plants can support the operation of power systems by participating in energy and ancillary service markets, just like traditional power plants.

[0005] Thus, the virtual power plant operation technology is generated, that is, how the virtual power plant aggregates multiple types of load side resources to improve its profit and reduce its cost in the above process. When providing frequency modulation service in the power market environment, the virtual power plant must first determine its frequency modulation market bid, that is, the frequency modulation capacity in different time periods. Then, when actually providing frequency modulation service, the virtual power plant decomposes the required net power adjustment to its internal resources to respond to the frequency modulation signal received from the power grid. An ideal power decomposition strategy should be able to preferentially use low-cost resources to respond to the frequency modulation signal. However, in general, it is much more complex to achieve optimal power decomposition, because the operation of resources such as electric vehicles and temperature-controlled loads, which are similar to energy storage, is time-coupled, which means that controlling the output of a resource in a time period may also affect the profit of the virtual power plant in subsequent time periods. Therefore, existing research on virtual power plants providing frequency modulation service adopts different degrees of simplification. The most common simplification method is proportional decomposition, which assumes that the response of each resource to the frequency modulation signal is proportional to the frequency modulation capacity allocated to it in advance. However, the proportional decomposition strategy does not effectively utilize the heterogeneity between resources to improve the operation of the virtual power plant, that is, low-cost resources are not preferentially called. Another intuitive idea is to set response priorities in the power decomposition process according to the operating characteristics of each resource to achieve a specific goal. However, these methods focus on responding to the current frequency modulation signal and cannot achieve optimization over the entire time period. Therefore, they are essentially heuristic methods and cannot guarantee long-term optimality.

[0006] SUMMARY

[0007] The present disclosure aims to at least partially solve one of the technical problems in the related art.

[0008] The first aspect embodiment of the present disclosure proposes a second-level optimization control method for joint frequency modulation of heterogeneous load resources, comprising:

[0009] Establish a standardized operation model of heterogeneous load resources, including a single energy consumption model and a network coupling relationship;

[0010] According to the parameters of the standardized operation model of each resource and the historical frequency modulation signal data of the frequency modulation market, predict the frequency modulation signal distribution and price distribution of the future time period;

[0011] Based on the frequency modulation signal distribution and price distribution, solve an optimal operation model containing multiple heterogeneous resources jointly participating in the energy and frequency modulation market to determine the baseline energy output and frequency modulation capacity;

[0012] According to the baseline energy output and frequency modulation capacity, construct an optimal decomposition problem of load resources and perform variable substitution to calculate the marginal cost and adjustable range of power adjustment of each resource;

[0013] In response to receiving the frequency modulation signal sent by the power grid, power of each load-side resource is updated from the resource with the lowest marginal regulation cost until the power adjustment requirement is met.

[0014] In some embodiments, the standardized operation model includes operation characteristics of all typical resources, and the standardized operation model is expressed as:

[0015] wherein subscripts t, s and i respectively represent variables related to time interval t, scenario s and resource i, underlined / overlined parameters are used to represent lower limit / upper limit of the corresponding variable, superscript dis represents discharging to the power grid, superscript ch represents charging from the power grid, p t,s,i is the net power of a single resource, and is the sum of power discharged to the power grid and power charged from the power grid, e represents the state of the resource, brackets represent that the formula is valid for and respectively, are power discharged to the power grid and power charged from the power grid, are lower limit and upper limit of power discharged to the power grid or charged from the power grid, e t+1,i is the state of the resource at t+1, e t,i is the state of the resource e at t, t,i are lower limit and upper limit of the state of resource i at t, is energy conversion efficiency of resource j to resource i when discharging to the power grid, is energy conversion efficiency of resource j to resource i when charging from the power grid, Δt is time period length, w t,i is the rate of change of the state of resource i caused by external environment in time period t, is a dual variable corresponding to the constraint, is the initial state of resource i, θ i is the state dissipation rate of resource i, π t,s is the probability that the frequency modulation signal value falls in interval s in time period t.

[0016] In some embodiments, the prediction of frequency modulation signal distribution and price distribution in the future time period according to the parameters of the standardized operation model of each resource and historical frequency modulation signal data of the frequency modulation market includes:

[0017] The parameters of the standardized operation model of each resource are obtained, and matrix modeling is performed, and the expression is: ​

[0018] wherein P dis , P ch are power matrices discharging to the grid, charging from the grid, respectively, P dis(ch) , are lower, upper bounds of power discharging to the grid, charging from the grid, respectively, E t is the state of resources at t+1, E t , are lower, upper bounds of state of resources at t, H (dis)ch is the energy conversion efficiency matrix of resources, whose elements are Θ, W t , Π t are vectors composed of θ i , w t,i , π t,s , respectively.

[0019] Obtaining historical frequency modulation signal data of the frequency modulation market, combining the model expression of the matrix modeling to predict the frequency modulation market and energy market price of a future period, and the frequency modulation signal distribution of the future period.

[0020] In some embodiments, the optimal operation model of the multi-element heterogeneous resource joint participation in the energy and frequency modulation market is solved based on the frequency modulation signal distribution and the price distribution, to determine the benchmark energy output and the frequency modulation capacity, comprising:

[0021] An aggregated power constraint of the virtual power plant is established, and the expression is:

[0022] wherein, is the baseline power of the virtual power plant in the energy at time period t, r t is the frequency modulation capacity of the virtual power plant at time period t, δ s is the frequency modulation signal of the virtual power plant in scenario s, the product of the frequency modulation signal δ s and r t is the output to be adjusted of the virtual power plant in scenario s.

[0023] A maximum output maintenance time constraint is established, and for any t, resource i satisfies:

[0024] wherein, the scenario with δ s =1 and the scenario with δ s =-1 represent the maximum output scenario, Δt req represents the preset maximum output maintenance time.

[0025] The resource response cost is calculated according to a cost function under the premise of satisfying the aggregate power constraint and the maximum output maintenance time constraint, and the expression of the cost function is:

[0026] wherein, Cost t represents the resource response cost at time t, Pr is a cost coefficient, is a cost coefficient of the resource i when discharging to the power grid, is a cost coefficient of the resource i when charging from the power grid;

[0027] The optimization objective of the virtual power plant is to maximize its market profit in the entire time range, and the virtual power plant operation profit is calculated, and the expression is:

[0028] wherein, Profit represents the operation profit, and superscripts e, r, cap and mil represent energy, frequency modulation, frequency modulation capacity and frequency modulation mileage, represents the income of the energy market, is the baseline power of the virtual power plant in the energy of the time period t, r t is the frequency modulation capacity of the virtual power plant in the time period t, is the energy exchanged with the power grid, represents the income of the frequency modulation market, s perf is the performance score of the virtual power plant, is the expected frequency modulation mileage of the virtual power plant in the time period t.

[0029] In some embodiments, according to the baseline energy output and frequency modulation capacity, a load resource optimal decomposition problem is constructed and variable substitution is performed, and the marginal cost and adjustable range of power adjustment of each resource are calculated, including:

[0030] The optimization objective of the virtual power plant is to minimize the cost generated by responding to the frequency modulation signal in the effective period of the frequency modulation signal , and a load resource optimal decomposition problem is constructed, and the expression is:

[0031] wherein, is the immediate operating cost, is the influence cost of the total profit of the virtual power plant in the entire time domain after the state change of the resource, is the Lagrange multiplier corresponding to the state constraint of the resource j and the time period t at the optimal solution of the optimal energy utilization problem;

[0032] The original power segment is numbered to inject positive to the grid, and the output of the discharge segment and the charge segment is obtained, and the expression is as follows:

[0033] Wherein, pk represents the output of an arbitrary power segment, pk 放 (i) represents the output of the discharge power segment, pk 充 (i): represents the output of the charge power segment;

[0034] For the equivalent cost coefficients of the discharge and charge power segments, the following expression is used for replacement:

[0035] Wherein, c k放 (i): represents the equivalent cost coefficient of the discharge power segment after replacement, c k充 (i): represents the equivalent cost coefficient of the charge power segment after replacement;

[0036] According to the output and the equivalent cost coefficient of the discharge segment / charge segment after replacement, the load resource optimal decomposition problem is rewritten, and the marginal cost and the adjustable range of the power adjustment of each resource are obtained, and the expression is as follows:

[0037] Wherein, is the net output required by the virtual power plant, is the lower / upper limit of the power segment k, which is obtained by substituting and respectively.

[0038] In some embodiments, the power of each load-side resource is updated starting from the resource with the lowest marginal adjustment cost in response to receiving the frequency modulation signal sent by the power grid until the power adjustment requirement is met, including:

[0039] In response to receiving the frequency modulation signal sent by the power grid, the power of each load-side resource is updated starting from the power segment with the smallest cost coefficient by using an algorithm involving only algebraic operations until the total power reaches the preset adjustment requirement, wherein the output of each power segment in the last response is used as the initial value when starting to adjust the power;

[0040] Based on variable substitution, the actual output of each resource is obtained by reverse substitution, and is executed through a control means.

[0041] The second aspect embodiment of the present disclosure proposes a second-level optimization control device for joint frequency modulation of heterogeneous load resources, including:

[0042] a standardized operation model construction module configured to establish a standardized operation model of the heterogeneous load resources, including a single-body energy consumption model and a network coupling relationship;

[0043] a prediction module configured to predict a frequency modulation signal distribution and a price distribution of a future period according to parameters of the standardized operation model of each resource and historical frequency modulation signal data of a frequency modulation market;

[0044] a solution module configured to solve an optimal operation model of joint participation of multiple heterogeneous resources in an energy and frequency modulation market based on the frequency modulation signal distribution and the price distribution, and determine a benchmark energy output and a frequency modulation capacity;

[0045] a resource optimal decomposition module configured to construct a load resource optimal decomposition problem and perform variable substitution according to the benchmark energy output and the frequency modulation capacity, and calculate marginal costs and adjustable ranges of power adjustment of each resource;

[0046] a frequency modulation module configured to, in response to receiving a frequency modulation signal sent by a power grid, start calling from a resource with the lowest marginal adjustment cost, update power of each load-side resource until a power adjustment requirement is met.

[0047] A third aspect of the present disclosure provides an electronic device, comprising:

[0048] at least one processor; and

[0049] a memory in communication connection with the at least one processor,

[0050] wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to implement the second aspect of the present disclosure.

[0051] A fourth aspect of the present disclosure provides a computer-readable storage medium, wherein when instructions in the storage medium are executed by a processor corresponding to the wire harness selection system, the wire harness selection system can implement the second aspect of the present disclosure.

[0052] A fifth aspect of the present disclosure provides a computer program product, comprising a computer program, which, when executed by a processor, implements the second aspect of the present disclosure.

[0053] A sixth aspect of the present disclosure provides a computer program comprising computer program code, which, when executed on a computer, causes the computer to perform the second aspect of the present disclosure.

[0054] The technical solutions provided by the embodiments of the present disclosure have at least the following beneficial effects:

[0055] By establishing a standardized operation model of heterogeneous load resources, including a single-body energy consumption model and a network coupling relationship; according to the parameters of the standardized operation model of each resource and the historical frequency modulation signal data of the frequency modulation market, the frequency modulation signal distribution and price distribution of the future period are predicted; based on the frequency modulation signal distribution and price distribution, the optimal operation model of the multi-element heterogeneous resource joint participation energy and frequency modulation market is solved, and the benchmark energy output and frequency modulation capacity are determined; according to the benchmark energy output and frequency modulation capacity, the optimal decomposition problem of load resources is constructed and variable substitution is performed, and the marginal cost and adjustable range of power adjustment of each resource are calculated; in response to receiving the frequency modulation signal sent by the power grid, the resources with the lowest marginal adjustment cost are called first, and the power of each load-side resource is updated until the power adjustment requirement is met. When the virtual power plant aggregates multiple heterogeneous resources to provide frequency modulation services, low-cost resources can be called first and the time coupling characteristics are considered, and the complementary characteristics of different types of resources are used to achieve optimal control; at the same time, considering the real-time following demand of the frequency modulation signal, a fast solving algorithm is designed to realize a response of seconds, thereby improving the economy of load regulation and better utilizing the demand side flexibility resources.

[0056] Additional aspects and advantages of the present disclosure will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0057] The above and / or additional aspects and advantages of the present disclosure will become apparent and be readily appreciated from the following description, taken in conjunction with the accompanying drawings, in which:

[0058] FIG. 1 is a flowchart of a method for second-level optimization control of joint frequency modulation of heterogeneous load resources according to an embodiment of the present disclosure;

[0059] FIG. 2 is a block diagram of a device for second-level optimization control of joint frequency modulation of heterogeneous load resources according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0060] Embodiments of the present disclosure are described in detail below, examples of which are shown in the accompanying drawings, in which the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below are exemplary and are intended to explain the present disclosure, and cannot be understood as a limitation of the present disclosure.

[0061] The background art related to the present disclosure includes:

[0062] 1) Load-side resource modeling technique: The output of distributed photovoltaic and wind power is limited by the maximum available power generation. Due to thermal inertia, the temperature of the temperature-controlled load can be analogized to the state of charge in the energy storage, thereby establishing a battery model of the temperature-controlled load. The modeling of industrial production processes is based on a linearized state task network model, including the power limit, material buffer limit, initial value and production target of the industrial production process.

[0063] 2) Frequency modulation signal prediction technique: In the power market environment, the frequency modulation signal (also known as automatic generation control (AGC) number) sent by the dispatching institution to the frequency modulation resource has a certain randomness and cannot be determined in advance, but its distribution can be predicted according to historical data and related factors;

[0064] 3) Stochastic optimization technique: Stochastic optimization refers to an optimization problem with random factors, which is solved by using tools such as probability statistics, stochastic process and stochastic analysis. The goal is usually to maximize the expected profit.

[0065] A second aspect of the present disclosure provides a kind of heterogeneous load resource joint frequency modulation second-level optimization control method and device.

[0066] In the embodiments of the present disclosure, subscripts t, s and i represent variables related to time interval t, scene s and resource i respectively, and the parameters with underlined / superscript are used to represent the lower limit / upper limit of the corresponding variable, and the superscript dis represents discharging to the power grid, and the superscript ch represents charging to the power grid.

[0067] Fig. 1 is a flowchart of a second-level optimization control method for heterogeneous load resource joint frequency modulation according to the first aspect of the present disclosure, as shown in Fig. 1, the method comprises the following steps 101-105.

[0068] Step 101, establish a standardized operation model of heterogeneous load resources, including single energy consumption model and network coupling relationship.

[0069] It can be understood that before the heterogeneous load resources are jointly frequency modulated, a standardized operation model of the heterogeneous load resources needs to be established, and the standardized model contains the operation characteristics of all typical resources. For a specific resource, its operation model can be regarded as a degenerate form of the standardized model.

[0070] Specifically, the output of distributed photovoltaic and wind power is limited by the maximum available power generation. For energy storage, the power upper bound and other related coefficients are actually constants. The operation model of electric vehicles is similar to that of energy storage, except that the plug-in state (indicating whether the electric vehicle is connected to the charging pile) changes, so the related power or energy upper bound can be time-varying. The characteristics of delayable loads are similar to those of electric vehicles, the main feature being that the total power consumption must reach a value acceptable to the user within a certain period of time. Due to thermal inertia, the temperature of a temperature-controlled load can be analogized to the state of charge in energy storage, thereby establishing a battery model for temperature-controlled loads. Moreover, room temperature requirements can change over time, resulting in time-varying. is the production efficiency between industrial production processes, representing the change in material i due to the energy consumption of production link j. This can be described in the form of a coefficient matrix, and for other types of resources such as energy storage, only the diagonal elements are non-zero (i.e., the charging efficiency of energy storage i), making it a degenerate case of the model.

[0071] The expressions of the normalized model are as follows, representing power decomposition, power upper and lower bounds, energy upper and lower bounds, energy conversion, and initial energy, respectively:

[0072] where p t,s,i is the net power of a single resource, which is the sum of the power discharged to the grid and the power charged from the grid, e represents the state of the resource, the brackets represent that the formula is valid for and respectively, and are the power lower and upper bounds of discharging to the grid or charging from the grid, e t+1,i is the resource state at t+1, e t,i is the resource state e t,i , are the state lower and upper bounds of resource i at t, is the energy conversion efficiency of resource j to resource i when discharging to the grid, is the energy conversion efficiency of resource j to resource i when charging from the grid, for resources such as energy storage, only when i=j, is non-zero, but this is not necessarily the case for industrial production processes, and Δt is the time period length, w t,i is the rate of change of the state of resource i due to external environment at time period t, is the dual variable corresponding to the constraint, is the initial state of resource i, θ i is the state dissipation rate of resource i, π t,s is the probability of the frequency modulation signal value falling in the interval s within the time period t.

[0073] It should be noted that for resources that cannot meet the frequency modulation service requirements, it is assumed that their power is constant within a specific time interval, and they do not participate in the response of the virtual power plant to the frequency modulation signal.

[0074] In some embodiments, the following constraints are added to the optimal energy utilization model for the corresponding resources, and the output is fixed at the reference power when deploying frequency modulation:

[0075] wherein is the reference discharge (charge) power of resource i at time period t.

[0076] Step 102, according to the parameters of the standardized operation model of each resource and the historical frequency modulation signal data of the frequency modulation market, predict the frequency modulation signal distribution and price distribution of the future time period.

[0077] In the embodiments of the present disclosure, first, the parameters of the standardized operation model of each resource are obtained, and matrix modeling is performed thereon, and the expression is:

[0078] wherein, P dis , P ch are the power matrices discharged to the grid and charged from the grid, P dis(ch) , are the lower and upper limits of the power discharged to the grid and charged from the grid, E t is the state of the resource at time t+1, E t , are the state lower and upper limit vectors of the resource at time t, H (dis)ch is the resource energy conversion efficiency matrix, and the elements are Θ, W t , Π t are vectors composed of θ i , w t,i , π t,s .

[0079] It should be noted that the matrix form expression has the same meaning as the expression of the standardized operation model, but the matrix form data is convenient for computer storage and processing.

[0080] Then, the historical frequency modulation signal data of the frequency modulation market is acquired, and the model expression of the matrix modeling is combined to predict the frequency modulation market and energy market price of a future period and the frequency modulation signal distribution of the future period.

[0081] In some embodiments, let t represent a period of interest, and let s represent a frequency modulation signal value interval, then the probability π t,s of the frequency modulation signal value falling in the interval s (i.e., the frequency modulation signal scene s occurs) in the period t can be estimated according to the total number of frequency modulation signals that have occurred in the period in the past few days.

[0082] In step 103, based on the frequency modulation signal distribution and the price distribution, an optimal operation model of a multi-element heterogeneous resource jointly participating in the energy and frequency modulation market is solved to determine the baseline energy output and the frequency modulation capacity.

[0083] It should be noted that when solving the optimal operation model of the multi-element heterogeneous resource jointly participating in the energy and frequency modulation market, the aggregated power constraint and the maximum output maintenance time constraint need to be met.

[0084] Specifically, in each dispatching scene, the total power of all resources must follow the instructions sent by the grid, and the expression of the aggregated power constraint of the virtual power plant is:

[0085] wherein, is the baseline power of the virtual power plant in the energy in the period t, r t is the frequency modulation capacity of the virtual power plant in the period t, δ s is the frequency modulation signal of the virtual power plant in the scene s, the frequency modulation signal δ s and r t are the products of the virtual power plant in the scene s to be adjusted.

[0086] In addition, in order to ensure the reliability of the frequency modulation capacity, i.e., under extreme conditions, the frequency modulation resource can continuously provide the deployed frequency modulation, the grid operator usually requires the frequency modulation resource to maintain the maximum output for a period of time Δt req , i.e., for any t, the resource i satisfies the following maximum output maintenance time constraint, the expression is:

[0087] wherein, the scene of δ s = 1 and the scene of δ s = -1 represent the maximum output scene, and Δt req represents the preset maximum output maintenance time.

[0088] It is noted that for resources with energy constraints, the longer the required duration, the smaller the available frequency modulation capacity that can be typically used. This essentially increases the conservatism of the frequency modulation capacity to cope with the uncertainty of the frequency modulation signal.

[0089] In addition, the cost function is given in piecewise linear form, where the cost coefficient Pr can also be understood as the compensation price negotiated between the resource owner and the grid operator.

[0090] In some embodiments, the cost coefficient is rewarded at 120% of the cost. If necessary, a more accurate piecewise linear model can also be used without affecting the linear nature of the model.

[0091] The expression of the cost function is:

[0092] where Cost t represents the resource response cost at time t, Pr is the cost coefficient, is the cost coefficient of resource i when discharging to the grid, is the cost coefficient of resource i when charging from the grid.

[0093] Finally, under the premise of meeting the above constraints, the optimization objective of the virtual power plant is to maximize its own market profit in the entire time range, and the expression of the virtual power plant operation profit is:

[0094] where Profit represents the operation profit, and superscripts e, r, cap and mil represent energy, frequency modulation, frequency modulation capacity and frequency modulation mileage, represents the income of the energy market, is the baseline power of the virtual power plant in the energy of time period t, r t is the frequency modulation capacity of the virtual power plant in time period t, is the energy exchanged with the grid, represents the income of the frequency modulation market, s perf is the performance score of the virtual power plant, is the expected frequency modulation mileage of the virtual power plant in time period t.

[0095] It can be understood that s perf and are both considered as known parameters given by the grid operator.

[0096] Step 104, according to the reference energy output and the frequency modulation capacity, construct the optimal decomposition problem of the load resource and perform variable substitution, calculate the marginal cost and adjustable range of power adjustment of each resource.

[0097] The virtual power plant receives the frequency modulation signal δ s After the validity period of the frequency modulation signal , the output of each resource is determined until the next frequency modulation signal is received.

[0098] In the embodiments of the present disclosure, only the current period (t = 1) and a specific frequency modulation signal are concerned, but for formal consistency, the subscript t and s are retained here, and the optimal load resource decomposition problem constructed can be expressed as:

[0099] Among them, the goal of the virtual power plant is to minimize the cost generated in response to the frequency modulation signal in , including the immediate operating cost and the cost of the total profit of the virtual power plant in the entire time domain after the change of the resource state is the Lagrange multiplier corresponding to the state constraint of resource j and period t at the optimal solution of the optimal energy utilization problem.

[0100] Then, the original power segment is numbered with k, that is, pk represents the output of any power segment, and the output of the discharge segment and the charging segment is positive to inject into the grid, and the expressions are respectively:

[0101] Among them, pk represents the output of any power segment, and pk 放 (i) represents the output of the discharge power segment, and pk 充 (i): represents the output of the charging power segment, and for the resource with segmented linear cost, each segment can be represented respectively.

[0102] For the equivalent cost coefficients of the discharge and charging power segments, the following expression is used for replacement:

[0103] Among them, c k放 (i): represents the equivalent cost coefficient of the discharge power segment after replacement, and c k充 (i): represents the equivalent cost coefficient of the charging power segment after replacement.

[0104] Then, after replacing pk and ck, that is, according to the output and equivalent cost coefficient of the replaced discharge segment / charging segment, the optimal load resource decomposition problem is rewritten, and the marginal cost and adjustable range of power adjustment of each resource are obtained, and the expression is:

[0105] wherein, is the net output required by the virtual power plant, is the lower / upper limit of the power segment k, which is obtained by substituting and respectively.

[0106] Step 105, in response to receiving the frequency modulation signal sent by the power grid, starting from the resource with the lowest marginal regulation cost, updating the power of each load-side resource until the power adjustment requirement is met.

[0107] Based on further analysis of the problem structure, the embodiments of the present disclosure propose an algorithm involving only algebraic operations to reduce the computational complexity.

[0108] Specifically, after receiving the frequency modulation signal sent by the power grid, the power of each load-side resource is updated by starting from the power segment with the smallest cost coefficient using an algorithm involving only algebraic operations until the total power reaches the preset adjustment requirement. When starting to adjust the power, the output of each power segment in the last response is used as the initial value.

[0109] It should be noted that in actual operation, the output of each power segment in the last response can be used as the initial value instead of reinitializing each time, thereby further reducing the amount of calculation. When the change rate of the frequency modulation signal is small relative to the length of the power segment of the resource, only the output of the marginal resource needs to be updated, and other resources operate at the upper and lower power boundaries, which is easier to implement for power control.

[0110] Finally, according to the variable substitution process mentioned in step 104, the actual output of each resource is obtained by reverse substitution, and is executed through control means.

[0111] It can be understood that the purpose of the control means is to execute the calculated optimal control result, and in some embodiments, the power of each resource is adjusted to make the actual output reach the optimal value given by the algorithm.

[0112] The embodiments of the present disclosure can prioritize low-cost resources and consider time coupling characteristics when providing frequency modulation services for virtual power plants aggregating multiple heterogeneous resources, and utilize the complementary characteristics of different types of resources to achieve optimal control. At the same time, considering the real-time following demand of the frequency modulation signal, a fast solving algorithm is designed to achieve a response of seconds, thereby improving the economy of load regulation and better utilizing the flexibility of demand-side resources.

[0113] FIG. 2 is a block diagram of a second aspect of a heterogeneous load resource joint frequency modulation second-level optimization control device 10 according to an embodiment of the present disclosure, which includes:

[0114] The standardized operation model construction module 100 is configured to establish a standardized operation model of the heterogeneous load resource, including a monomer energy consumption model and a network coupling relationship.

[0115] The prediction module 200 is configured to predict a frequency modulation signal distribution and a price distribution of a future period according to parameters of the standardized operation model of each resource and historical frequency modulation signal data of the frequency modulation market.

[0116] The solving module 300 is configured to solve an optimal operation model of the multi-element heterogeneous resource joint participation in the energy and frequency modulation market based on the frequency modulation signal distribution and the price distribution, to determine a benchmark energy output and a frequency modulation capacity.

[0117] The resource optimal decomposition module 400 is configured to construct a load resource optimal decomposition problem and perform variable substitution according to the benchmark energy output and the frequency modulation capacity, to calculate marginal costs and adjustable ranges of power adjustment of each resource.

[0118] The frequency modulation module 500 is configured to start calling from a resource with the lowest marginal adjustment cost in response to receiving a frequency modulation signal sent by the power grid, to update power of each load-side resource until a power adjustment requirement is met.

[0119] As to the apparatus in the above-mentioned embodiments, the specific manner in which each module performs operations has been described in detail in the embodiments of the method, and thus will not be described in detail here.

[0120] The third aspect of the present disclosure provides an electronic device, comprising:

[0121] at least one processor; and

[0122] a memory in communication with the at least one processor,

[0123] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to implement the second aspect of the method for second-level optimization control of joint frequency modulation of heterogeneous load resources.

[0124] The fourth aspect of the present disclosure provides a computer-readable storage medium, wherein when the instructions in the storage medium are executed by the processor corresponding to the wire harness selection system, the wire harness selection system can implement the second aspect of the method for second-level optimization control of joint frequency modulation of heterogeneous load resources.

[0125] The fifth aspect of the present disclosure provides a computer program product, comprising a computer program, which, when executed by a processor, implements the second aspect of the method for second-level optimization control of joint frequency modulation of heterogeneous load resources.

[0126] The sixth aspect of the present disclosure provides a computer program including computer program code, when the computer program code is executed on a computer, so that the computer executes the second-level optimization control method of the heterogeneous load resource joint frequency modulation described in any embodiment of the first aspect.

[0127] It should be understood that the steps shown above can be reordered, added, or deleted using various forms of flow. For example, each step described in the present disclosure can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions of the present disclosure can be achieved, which is not limited herein.

[0128] The above specific embodiments do not constitute a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present disclosure shall be included in the protection scope of the present disclosure.

[0129] It should be noted that the foregoing embodiments of the second-level optimization control method of the heterogeneous load resource joint frequency modulation are also applicable to the apparatus, device, readable storage medium, computer program product, and computer program of the embodiments of the present disclosure, which will not be repeated here.

[0130] All embodiments of the present disclosure can be executed independently or in combination with other embodiments, and are considered to be within the protection scope required by the present disclosure.

Claims

1. A second-level optimization control method for joint frequency modulation of heterogeneous load resources, comprising: Establish a standardized operation model for heterogeneous load resources, including individual energy consumption models and network coupling relationships; Based on the parameters of the standardized operation model of each resource and the historical FM signal data of the FM market, the FM signal distribution and price distribution in the future period are predicted; Based on the frequency modulation signal distribution and price distribution, an optimal operation model involving multiple heterogeneous resources jointly participating in the energy and frequency modulation market is solved to determine the benchmark energy output and frequency modulation capacity; Based on the benchmark energy output and frequency regulation capacity, construct an optimal decomposition problem for load resources and perform variable substitution to calculate the marginal cost and adjustable range of power adjustment for each resource; In response to receiving the frequency regulation signal sent by the power grid, the resource with the lowest marginal regulation cost is called up first, and the power of each load-side resource is updated until the power regulation requirements are met.

2. The method according to claim 1, wherein the standardized operation model includes the operation characteristics of all typical resources, and the expression form of the standardized operation model is: in, Subscripts t, s, and i denote variables related to time interval t, scenario s, and resource i, respectively. Parameters with underscores / overscores are used to indicate the lower / upper limits of the corresponding variables. Superscript dis denotes discharging to the grid, and superscript ch denotes charging to the grid. t,s,i is the net power of a single resource, which is the sum of the power discharged to the grid and charged from the grid. e represents the state of the resource. The brackets represent the and Established separately, are the power discharged to the grid and the power charged from the grid, are the lower and upper limits of power discharging to or charging from the grid, respectively, e t+1,i is the resource status at time t+1, e t,i is the resource state e at time t t,i 、 They are the lower limit of the state of resource i at time t, Upper limit, is the energy conversion efficiency of resource j to resource i when discharging to the grid, is the energy conversion efficiency of resource j to resource i when charging from the grid, Δt is the time period, w t,i is the rate of change of the state of resource i in time period t caused by the external environment, is the dual variable of the corresponding constraint, is the initial state of resource i, θ i is the state dissipation rate of resource i, π t,s is the probability that the FM signal value falls within interval s during time period t.

3. The method according to claim 2, wherein the step of predicting the distribution of FM signals and price distribution in future time periods based on the parameters of the standardized operation model of each resource and the historical FM signal data of the FM market comprises: Obtain the parameters of the standardized operation model of each resource and perform matrix modeling. The expression is: Among them, P dis 、P ch They are the power matrices for discharging to the grid and charging from the grid, P dis(ch) 、 are the lower and upper limits of power discharging to and charging from the grid, respectively, E t is the resource status at time t+1, E t 、 They are the lower and upper state vectors of the resource at time t, H (dis)ch is the resource energy conversion efficiency matrix, whose elements are Θ、W t 、Π t They are θ i 、w t,i , π t,s The vector formed; Obtain historical FM signal data from the FM market, and combine it with the matrix modeling model expression to predict the FM market and energy market prices in the future period, as well as the FM signal distribution in the future period.

4. The method according to claim 3, wherein solving an optimal operation model involving multiple heterogeneous resources jointly participating in the energy and frequency regulation market based on the frequency regulation signal distribution and price distribution to determine the benchmark energy output and frequency regulation capacity comprises: The aggregate power constraint of the virtual power plant is established as follows: in, is the baseline power of the virtual power plant in the energy of time period t, r t is the frequency regulation capacity of the virtual power plant in time period t, δ s is the frequency modulation signal of the virtual power plant in scenario s, and the frequency modulation signal δ s and r t The product of is the output of the virtual power plant to be adjusted in scenario s; Establish the maximum output maintenance time constraint. For any t, resource i satisfies: Among them, δ s = 1 scenario and δ s = -1 represents the maximum output scenario, Δt req Indicates the preset maximum output maintenance time; Under the premise of satisfying the aggregate power constraint and the maximum output maintenance time constraint, the resource response cost is calculated according to the cost function, and the expression of the cost function is: Among them, Cost t represents the resource response cost at time t, Pr is the cost coefficient, is the cost coefficient of resource i when discharging to the grid, is the cost coefficient of resource i when charging from the grid; The optimization goal of the virtual power plant is to maximize its own market profit over the entire time range. The operating profit of the virtual power plant is calculated as follows: Among them, Profit represents operating profit, and the superscripts e, r, cap, and mil represent energy, frequency regulation, frequency regulation capacity, and frequency regulation mileage, respectively. represents the revenue of the energy market, is the baseline power of the virtual power plant in the energy of time period t, r t is the frequency regulation capacity of the virtual power plant in time period t, It is the energy exchanged with the power grid. represents the revenue of the FM market, s perf is the performance score of the virtual power plant, is the expected frequency regulation mileage of the virtual power plant in time period t.

5. The method according to claim 4, wherein, based on the benchmark energy output and frequency regulation capacity, constructing a load resource optimal decomposition problem and performing variable substitution to calculate the marginal cost and adjustable range of power adjustment of each resource, comprises: The optimization goal of virtual power plant is to minimize the The cost generated by the internal response frequency modulation signal is used to construct the optimal decomposition problem of load resources, which is expressed as: in, For immediate operating costs, is the impact cost of the total profit of the virtual power plant in the entire time domain after the resource status changes, is the Lagrange multiplier corresponding to the state constraints of resource j and time period t at the optimal solution of the optimal energy use problem; The original power segments are numbered, with injection into the grid as positive, and the outputs of the discharge segment and the charge segment are obtained, and the expressions are: Among them, pk represents the output of any power segment, pk 放 (i) represents the output of the discharge power stage, pk 充 (i): represents the output of the charging power segment; For the equivalent cost coefficients of the discharging and charging power segments, substitute according to the following expressions: Among them, c k放 (i): represents the equivalent cost coefficient after the discharge power section is replaced, c k充 (i): represents the equivalent cost coefficient after the charging power segment is replaced; According to the output and equivalent cost coefficient of the replaced discharge / charging segment, the optimal decomposition problem of load resources is rewritten to obtain the marginal cost and adjustable range of power adjustment of each resource. The expression is: in, is the required net output of the virtual power plant, is the lower / upper limit of power band k, respectively Oversubstitution and get.

6. The method according to claim 5, wherein in response to receiving the frequency modulation signal sent by the power grid, starting with calling the resource with the lowest marginal regulation cost, updating the power of each load-side resource until the power adjustment requirement is met, comprises: In response to receiving a frequency modulation signal sent by the power grid, adjusting the power starting from the power segment with the minimum cost coefficient by an algorithm involving only algebraic operations, updating the power of each load-side resource until the total power reaches a preset adjustment requirement, wherein when starting to adjust the power, the output of each power segment in the last response is used as an initial value; Based on variable substitution, the actual output of each resource is obtained through reverse substitution and executed through control measures.

7. A second-level optimization control device for joint frequency modulation of heterogeneous load resources, comprising: Standardized operation model construction module, used to establish a standardized operation model for heterogeneous load resources, including single energy consumption model and network coupling relationship; The prediction module is used to predict the distribution of FM signals and price distribution in future time periods based on the parameters of the standardized operation model of each resource and the historical FM signal data of the FM market; A solution module, configured to solve an optimal operation model involving multiple heterogeneous resources jointly participating in the energy and frequency regulation market based on the frequency regulation signal distribution and price distribution, and determine a benchmark energy output and frequency regulation capacity; The resource optimal decomposition module is used to construct the load resource optimal decomposition problem and perform variable substitution based on the benchmark energy output and frequency regulation capacity, and calculate the marginal cost and adjustable range of each resource power adjustment; The frequency regulation module, in response to receiving the frequency regulation signal sent by the power grid, is used to start calling from the resource with the lowest marginal regulation cost and update the power of each load-side resource until the power regulation requirement is met.

8. An electronic device comprising: at least one processor; and a memory in communication with the at least one processor, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to implement the second-level optimization control method for joint frequency modulation of heterogeneous load resources as described in any one of claims 1 to 6.

9. A computer-readable storage medium, wherein when the instructions in the storage medium are executed by a processor corresponding to a wiring harness selection system, the wiring harness selection system is enabled to implement the second-level optimization control method for joint frequency modulation of heterogeneous load resources as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, wherein when executed by a processor, the computer program implements the second-level optimization control method for joint frequency modulation of heterogeneous load resources according to any one of claims 1 to 6.

11. A computer program comprising computer program code, which, when executed on a computer, enables the computer to execute the second-level optimization control method for joint frequency modulation of heterogeneous load resources according to any one of claims 1 to 6.

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